from PIL import Image
import torchvision.transforms as transforms
#使用PIL庫讀入圖片并進行resize
def ResizeImage():
    if not os.path.exists(rdir):
        os.makedirs(rdir)
    for i in range(10):
        im = Image.open(dir+str(i)+".jpg")
        im = im.resize((320,240),Image.BILINEAR)  #第一個參數(shù)為想要的size，第二個參數(shù)為插值方法，雙線性插值這里用的是
        im.save('{}/{}.jpg'.format(rdir, i))

#圖像隨機剪裁和中心剪裁
def crop(lib):
    for i in range(10):
        img = Image.open(lib+"/"+str(i)+".jpg")
        CenterCrop = transforms.CenterCrop((240,320))   #中心裁剪
        cropped_image = CenterCrop(img)  #PIL.Image.Image
        # im=np.array(cropped_image)  #可以將PIL.Image.Image轉成ndarry
        #cropped_image.show()    #將圖片顯示
        cropped_image.save('{}/cen_crop{}.jpg'.format(rdir, i))
        RandomCrop = transforms.RandomCrop(size=(240, 320))  #隨機剪裁
        random_image = RandomCrop(img)
        random_image.save('{}/rand_crop{}.jpg'.format(rdir, i))

#隨機旋轉
def random_rotation(lib):
    for i in range(10):
        img = Image.open(lib+"/"+str(i)+".jpg")
        RR = transforms.RandomRotation(degrees=(10, 80))   #degrees為隨機旋轉的角度
        rr_image = RR(img)
        rr_image.save('{}/rand_rotation{}.jpg'.format(rdir, i))

#圖片依概率翻轉,p為翻轉的概率
def horizontal_flip(lib):
    for i in range(10):
        img = Image.open(lib+"/"+str(i)+".jpg")
        HF = transforms.RandomHorizontalFlip(p=1.0)  #p為概率，缺省時默認0.5
        hf_image = HF(img)
        hf_image.save('{}/hori_flip{}.jpg'.format(rdir, i))

#encoding=utf-8
#author: walker
#date: 2014-05-15
#function: 更改圖片尺寸大小
from PIL import Image
'''
filein: 輸入圖片
fileout: 輸出圖片
width: 輸出圖片寬度
height:輸出圖片高度
type:輸出圖片類型（png, gif, jpeg...）
'''
def ResizeImage(filein, fileout, width, height, type):
? img = Image.open(filein)
? out = img.resize((width, height),Image.ANTIALIAS) #resize image with high-quality
? out.save(fileout, type)
if __name__ == "__main__":
? filein = r'0.jpg'
? fileout = r'testout.png'
? width = 6000
? height = 6000
? type = 'png'
? ResizeImage(filein, fileout, width, height, type)